Abstract
BackgroundGenome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.MethodsIn this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.ResultsWe found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).ConclusionsThis study offers powerful tools for exploring the functional consequences of variants generated from genome–phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
Highlights
Genome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; their functional roles are largely unclear
An integrative functional genomics framework We developed an integrative functional genomics framework to examine the functional regulation and tissuespecific regulatory circuits for large-scale disease-associated Single nucleotide polymorphisms (SNPs) reported in the genomewide association study (GWAS) Catalog and phenome-wide association study (PheWAS) Catalog (Fig. 1)
We found that one of the new network-predicted inflammatory bowel disease (IBD) genes in our reconstructed colon-specific transcription factor (TF)-target gene regulatory network (Additional file 6: figure S2), MAFB, was Together, this systematic investigation revealed that gene regulation plays important roles for significant trait-SNP associations derived from the PheWAS Catalog, which is comparable with the GWAS Catalog
Summary
Genome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; their functional roles are largely unclear. Genome-wide association studies (GWAS) have proven an effective strategy for the detection of variants statistically associated with disease or traits. Unlike GWAS, in which investigators examine the association of hundreds of thousands to a few million genotypes across the genome with a specific phenotype, PheWAS aims to detect the association of a specific genetic variant with a wide range of physiological and/or clinical outcomes categorized by disease terminologies like the International Classification of Disease (ICD) [4]. This study suggested that PheWAS could replicate known SNP-disease associations and identify potentially novel statistical associations Since this pioneer study, many other groups have applied this strategy to assess previously reported GWAS SNPs and managed to identify new associations and pleiotropic effects [5,6,7]. Even for the top 202 associations in the PheWAS Catalog, the current estimation of false positive rate for new associations could be as high as 29% [8]
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